Impact of lameness on behavior and productivity of lactating Holstein cows

Juárez, Solares; Robinson, P.H.; DePeters, E.J.; Price, Edward O.

doi:10.1016/s0168-1591(03)00107-2

Cited by 102 publications

(86 citation statements)

References 16 publications

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“…Several studies have reported increased lying times by lame cows. Juarez et al (2003) found that lame cows spent more time lying down; 25.2% of lame cows were lying down at the time of behavioral scans compared with 17.5% of sound cows. Similarly, Walker et al (2008) found that lame cows spent 6.3% more time lying down during estrus compared with sound cows.…”

Section: Discussionmentioning

confidence: 91%

“…In general, lame cows spent more time lying down (Singh et al, 1993;Walker et al, 2008;Chapinal et al, 2009) and less time feeding (González et al, 2008), performed fewer aggressive interactions (Galindo and Broom, 2002), and were less active (O'Callaghan et al, 2003) compared with nonlame cows. Juarez et al (2003) compared the lying behavior of cows with varying degrees of lameness and found that the proportion of cows lying down within each category increased with the degree of lameness. Cook et al (2004Cook et al ( , 2008 found that the type of lying surface affected the behavioral changes due to lameness; lame cows spent more time standing in stalls than nonlame cows, but this difference was greater with mattress stalls compared with sand stalls.…”

Section: Introductionmentioning

confidence: 99%

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Lying behavior as an indicator of lameness in dairy cows

Ito

Keyserlingk

LeBlanc

et al. 2010

Journal of Dairy Science

208

192

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Lameness is widely recognized as one of the most serious welfare and production concerns in the dairy industry. Our objectives were to evaluate the associations between lying behavior and lameness, and to determine whether lying behavior can be used as a diagnostic tool for lameness. Electronic data loggers recorded lying behavior of 1,319 cows from 28 farms at 1-min intervals for 5 d. These cows were gait scored according to a 5-point Numerical Rating System (NRS), and categorized as NRS ≤2, NRS = 3, or NRS = 4; no cow was scored as NRS = 5. Lameness was dichotomized twice: LAME (NRS ≥3) and SEVLAME (NRS = 4). Data were divided into 2 groups: 11 farms using deep-bedded stalls (DB) and 17 farms using mattress stalls (MAT). Differences in the daily lying time (h/d), frequency of lying bouts (n/d), duration of lying bouts (min/bout), and the standard deviation of bout duration (min/ bout) between LAME or SEVLAME cows and those that were not were tested using mixed models. Receiver operating characteristic curves were constructed to identify behavioral thresholds to distinguish SEVLAME cows from the rest. Odds ratios for SEVLAME were estimated using logistic regression. Overall, 28.5% of cows were LAME including 7.3% that were SEVLAME. The prevalence of SEVLAME was higher on MAT farms than on DB farms (9.3 ± 1.3 vs. 4.4 ± 1.2%, respectively). SEVLAME cows on DB farms spent 12.8 [confidence interval (CI): 12.0 to 13.7] h/d lying down compared with 11.2 (CI: 10.7 to 11.8) h/d for cows that were not SEVLAME. These cows had longer duration of lying bouts [95.3 (CI: 84.6 to 107.3) vs. 80.3 (CI: 74.9 to 86.1) min/bout] and greater SD of bout duration [44.4 (CI: 41.1 to 48.0) vs. 50.7 (CI: 44.1 to 58.3) min/bout]. There were no behavioral differences among lameness categories on MAT farms. Within DB farms, cows with lying times >14.5 h/d had 16.2 (5.8 to 45.2) times higher odds of being SEVLAME. Cows with average lying bouts >90 min/bout were at 3.0 (1.2 to 7.4) times higher odds of being SEVLAME, and cows with average SD of bout duration >55 min/bout were at 4.1 (1.7 to 9.9) times higher odds of being SEVLAME. These results show that high lying times, long lying bouts, and variability in the duration of lying bouts were associated with lameness, and that stall surface influenced the behavioral responses of lame cows.

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Section: Discussionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Lying behavior as an indicator of lameness in dairy cows

Ito

Keyserlingk

LeBlanc

et al. 2010

Journal of Dairy Science

208

192

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“…As leg and claw disorders are painful for a cow, she tries to cope with this pain by changing her walking and standing behavior (Juarez et al, 2003). Therefore, cows that already have pain will be detected by the sensor.…”

Section: Main Findingsmentioning

confidence: 99%

Invited review: Sensors to support health management on dairy farms

Rutten

Velthuis

Steeneveld

et al. 2013

Journal of Dairy Science

390

318

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Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow's status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found.

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“…Lame cows are in pain, show inappetence, decreased milk yield, and weight loss (Green et al, 2002;O'Callaghan et al, 2003). The behaviour of lame cows is also affected: they are more restless at milking, spend more time lying down and eat more slowly (Hassall et al, 1993;Juarez et al, 2003;O'Callaghan et al, 2003). Lameness is a frequent reason for culling (Booth et al, 2004); for instance, clinical lameness increased the risk of culling 6-12 times during the first two months of lactation in the study of Rajala-Schultz and Gröhn (1999).…”

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confidence: 99%

Factors contributing to the incidence and prevalence of lameness on Czech dairy farms

Dembélé¹,

Špinka²,

Stěhulová³

et al. 2006

Czech J. Anim. Sci.

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Twenty-four Czech dairy farms were visited to record lameness prevalence and to identify factors associated with high lameness prevalence at the farm level and/or increased lameness risk at the level of individual cows. All cows were checked for lameness and forty cows per farm were examined for overgrown claws, body dirtiness and skin lesions. The farm environment was scored between 1 (excellent) to 5 (very poor) in three different aspects: floor slipperiness, cow care quality, and housing quality. Data on hoof trimming schedules were obtained from farm managers. Lameness prevalence on farms was in a wide range from 6% to 42% (median 22%). At the farm level, floor slipperiness and poor animal care were associated with high lameness prevalence (Spearman correlations, P < 0.05), and the proportion of cows with overgrown claws tended to be associated with it (P < 0.10). The reported time schedules of hoof trimming (continuous trimming applied or not; and time elapsed since the whole herd was trimmed) were unrelated to either the prevalence of overgrown claws or the prevalence of lameness. Within farms, cows with overgrown claws and dirty cows were at an increased risk of being lame (multiple logistic regression, P < 0.05) and cows with skin lesions tended to be more lame (P < 0.10). The risk of lameness had an inverted U-shape dependence on age (P < 0.05), with cows at 7-8 years of age being the most endangered by lameness. We conclude that there is a large potential for lameness reduction on some Czech dairy farms through improving the cow care and reducing floor slipperiness, and that within farms, cows with overgrown claws and also dirty cows and cows with skin lesions should be given special attention since they are more likely to get lame.

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Impact of lameness on behavior and productivity of lactating Holstein cows

Cited by 102 publications

References 16 publications

Lying behavior as an indicator of lameness in dairy cows

Lying behavior as an indicator of lameness in dairy cows

Invited review: Sensors to support health management on dairy farms

Factors contributing to the incidence and prevalence of lameness on Czech dairy farms

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